An online flow-imaging particle counter and conventional water quality sensors detect drinking water contamination in the presence of normal water quality fluctuations

Abstract

Contamination detection in drinking water is crucial for water utilities in terms of public health; however, current online water quality sensors can be unresponsive to various possible contaminants consisting of particulate and dissolved content or require a constant supply of reagents and sample preparation. We used a two-line test environment connected to a drinking water distribution system with flow-imaging particle counters and conventional sensors to assess their responses to the injection of contaminants into one line, including stormwater, treated wastewater, wastewater, well water, and Escherichia coli, while simultaneously measuring responses to normal water quality fluctuations in the other line. These water quality fluctuations were detected with all of the conventional sensors (except conductivity) and with 3 out of 5 of the size- and shape-derived particle classes of the flow-imaging particle counter. The flow-imaging particle counter was able to detect all of the studied contaminants, e.g. municipal wastewater at 0.001% (v/v), while the oxidation-reduction potential sensor outperformed other conventional sensors, detecting the same wastewater at 0.03% (v/v). The presence of particles less than 1 µm in size was shown to be a generic parameter for the detection of particulates present in the studied contaminants; however, they manifested a considerable response to fluctuations which led to lower relative response to contaminants in comparison to larger particles. The particle size and class distributions of contaminants were different from those of drinking water, and thus monitoring particles larger than 1 µm or specific particle classes of flow-imaging particle counter, which are substantially more abundant in contaminated water than in pure drinking water, can improve the detection of contamination events. Water utilities could optimize contamination detection by selecting water quality parameters with a minimal response to quality fluctuations and/or a high relative response to contaminants.